Data-Driven Strategy Decisions in an Uncertain and Fast-Changing Environment
Combining thoughts from the “Balanced scorecard” and “Your strategy needs a strategy”, I’d like to discuss the effect of data-driven decisions in an adaptive or in a shaping environment. These environments share the characteristic that they are relatively unpredictable in their development in the future. They therefore demand of participating companies to employ strategies that are flexible to change in the industry environment. In the current age of information, with terms like big data and cloud computing, more and more (strategic) decisions are being based on data analysis and interpretation. These analyses take up time however, especially when performed for the first time, or when data is incomplete. I therefore believe that data-driven decisions in a fastmoving and changing environment, like companies in an adaptive or shaping environment, can be counterproductive. It is highly possible that by the time the analysis models have been created, ran and recommendations translated from the results, that these results are no longer valid, due to the changing environment. Take the example of “your strategy needs a strategy” on an adaptive industry like fashion. There have been multiple accounts of the fashion standard changing overnight, because some celebrity wore a piece of clothing that nobody else has ever worn before. Even a relatively fast, week long data analysis would become almost useless due to this switch.
Thinking about a way of dealing with the discussed problems by use of ‘data-driven decisions in highly uncertain environments’, I propose to prioritize that data is collected and updated on a very regular basis, for instance daily, and that the data-analysis models are dynamic. Dynamic means in this case that the results of the models are directly updated based on the new input, whenever a shift in the industry environment takes place. Management will then be able to adjust their (short term) strategy accordingly, giving them a headstart in comparison to non-dynamic model using companies.
Companies that have most to benefit from this approach include companies that have no or very limited amount of inventory stockpiles, can adjust their products rapidly and have no or short production times. Some examples include consumer services, like on demand delivery like with Postmates or privately owned residence rentals like AirBnB.
The added benefit of this approach, against non-data-driven decision making is clear. There is objectivity in data, causing it to be a more trustworthy foundation for decisions than mere observations and intuition. I would like to stress the importance of the word foundation in this previous sentence, as data analysis still requires interpretation, which depends strongly on skills, intuition and experience of employees and management.